Creating Unbiased Machine Learning Models by Design

Joseph L. Breeden and Eugenia Leonova
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Joseph L. Breeden: Deep Future Analytics LLC, 1600 Lena St., Suite E3, Santa Fe, NM 87505, USA
Eugenia Leonova: Deep Future Analytics LLC, 1600 Lena St., Suite E3, Santa Fe, NM 87505, USA

JRFM, 2021, vol. 14, issue 11, 1-15

Abstract: Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods. This work presents a modeling procedure that carefully builds models around protected class information in order to make sure that the final machine learning model is independent of protected class status, even in a nonlinear sense. This procedure works for any machine learning method. The procedure was tested on subprime credit card data combined with demographic data by zip code from the US Census. The census data serves as an imperfect proxy for borrower demographics but serves to illustrate the procedure.

Keywords: unintended bias; fair lending; multihorizon survival models; machine learning (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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